Neurofunctional underpinnings of individual differences in visual episodic memory performance

Episodic memory, the ability to consciously recollect information and its context, varies substantially among individuals. While prior fMRI studies have identified certain brain regions linked to successful memory encoding at a group level, their role in explaining individual memory differences remains largely unexplored. Here, we analyze fMRI data of 1,498 adults participating in a picture encoding task in a single MRI scanner. We find that individual differences in responsivity of the hippocampus, orbitofrontal cortex, and posterior cingulate cortex account for individual variability in episodic memory performance. While these regions also emerge in our group-level analysis, other regions, predominantly within the lateral occipital cortex, are related to successful memory encoding but not to individual memory variation. Furthermore, our network-based approach reveals a link between the responsivity of nine functional connectivity networks and individual memory variability. Our work provides insights into the neurofunctional correlates of individual differences in visual episodic memory performance.


Data Availability
Source data are provided as a Source Data file. The individual fMRI data generated in this study and necessary to reproduce the voxel-based and network-based results have been deposited in the Open Science Framework database under accession code https://osf.io/7nhsg. The individual pre-processed fMRI data are not publicly available due size limitations, but are available from the corresponding authors upon request.
The group-level statistical brain maps (subsequent memory effects, memorability-corrected subsequent memory effects, voxel-based brain behavior correlations of the encoding contrast, voxel-based brain behavior correlations of the subsequent memory effect contrast, functional connectivity networks with brain-behavior correlations, arousal-corrected subsequent memory effects, memorability effects) have been deposited on the NeuroVault database under the accession code http://neurovault.org/collections/14303/), and the full set of 60 ICs, as calculated from subsample 1, has been deposited on Figshare under the accession code 10.6084/m9.figshare.c.6679262.
The sample consists of individuals of all sexes. Sex/gender were determined by self-report. We did not perform sex-specific or gender-specific subanalyses. We used self-reported sex as a covariate due to its known effects on the fMRI measures.
We did not classify subjects regarding variables such as race, ethnicity or other socially relevant groupings.
The participants were healthy young individuals of all sexes (complete data n=1498; 930 females), aged 18 to 35 (25th percentile = 20, 75th percentile = 24; M = 22.44, SD = 3.31), The subjects were free of any lifetime neurological or psychiatric illness, and did not take any medication at the time of the experiment (except hormonal contraceptives). Covariates were: Age, sex, MRI scanner update (scanner batch effects), room for behavioral tasks (location batch effects) Distribution of the batch variables was as follows: -Scanner gradient batch effects: three categories, of sample sizes 105, 264 and 1129 -Scanner software batch effects: two categories, of sample sizes 283 and 1215 -Free recall task room effects: three categories, of sample sizes 705, 266 and 527 For the network-based brain-behavior correlation analysis, 13 subjects were excluded (see below, information included in the main manuscript). This sample consisted of 567 males and 918 females, aged 18 to 35 years (M=22.45, SD=3.32). Distribution of batch variables was as follows: -Scanner gradient batch effects: three categories, of sample sizes 103, 259 and 1123 -Scanner software batch effects: two categories, of sample sizes 278 and 1207 -Free recall task room effects: three categories, of sample sizes 694, 265 and 526

Recruitment
Ethics oversight Note that full information on the approval of the study protocol must also be provided in the manuscript.

Field-specific reporting
Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.

Life sciences
Behavioural & social sciences Ecological, evolutionary & environmental sciences For a reference copy of the document with all sections, see nature.com/documents/nr-reporting-summary-flat.pdf

Behavioural & social sciences study design
All studies must disclose on these points even when the disclosure is negative. Advertising was done mainly at the University of Basel, Switzerland, and in local newspapers. We do not assume presence of self-selection biases.
Ethics committee of the Canton of Basel, Switzerland One-group design, quantitative data, event-related fMRI design for contrast calculations.
The age range was selected due to the known effects of aging on measures of the variables of interest (fMRI data, memory performance). The sample is representative for that age category.
In this large-scale fMRI study, a convenience sampling strategy was used. No prior sample size calculation was performed. For fMRI studies in particular, large sample sizes are important for the detectability, reliability and validity of statistical effects. Large sample sizes are preferrable for whole-brain analyses. This issue has further been emphasized with regards to brain-behavior correlation analyses (e.g., Dubois & Adolphs, 2016, Cerebral Cortex). The selection of the large sample size was particularly relevant because one of the main objectives of this study was to tackle issues emerging from small sample sizes. Along similar lines, the study aimed to verify effects that have previously been reported in studies where smaller sample sizes had been used.
Neuroimaging data (structural, functional) was collected on an MRI scanner. Behavioral data (memory performance) was collected using paper-pencil. No one was present beside the researchers. The researchers were unaware of the research questions addressed in this study. There was only one study condition.
The Study went from May 2009 to March 2016.
The same sample was used for all analyses with the exception of the network-based brain-behavior correlation analysis: of the full sample of 1,498 subjects, thirteen subjects were not included in the network-based brain-behavior correlation analysis because their data was not available at the time-point of data analysis.
This work is based on a Study where data collection had already been completed at the time-point of data analysis. Participants were excluded from the analyses based on the following criteria: 1) they did not fulfill the inclusion criteria; 2) they did not complete the whole task; 3) the quality of the structural and/or functional data was low; 4) data was not available at the time of analysis.
There was one study group.

nature portfolio | reporting summary
April 2023 positive), as well as 24 scrambled pictures, each depicting a geometrical figure. Two primacy and two recency pictures were presented at the beginning and the end the session, respectively. Those 4 pictures were not considered for the analyses. Pictures were presented for 2.5 s in a quasi-randomized order so that at maximum four pictures of the same category occurred consecutively. The encoding session lasted around 20 minutes.
Picture encoding (valence and arousal ratings for IAPS pictures, size and shape ratings for geometrical figures). This data was not used for analysis in the current study. Free recall of IAPS pictures: paper-pencil description of pictures seen (an unannounced task) Structural (T1), functional (T2*)
Normalization incorporated the following four steps: 1) Structural images of each subject were segmented using the 'New Segment' procedure in SPM12.
2) The resulting gray and white matter images were used to derive a study-specific group template.
The template was computed from a subgroup of 1.000 subjects, which were part of the subjects included in the present study. 3) An affine transformation was applied to map the group template to MNI space. 4) Subject-to-template and template-to-MNI transformations were combined to map the functional images to MNI space. The functional images were smoothed with an isotropic 8 mm full-width at half-maximum (FWHM) Gaussian filter.
Volumes were slice-time corrected to the first slice, realigned using the 'register to mean' option, and coregistred to the anatomical image by applying a normalized mutual information 3-D rigid-body transformation. Successful coregistration was visually verified for each subject. Subject-to-template normalization was done using DARTEL (51), which allows registration to both cortical and subcortical regions and has been shown to perform well in volume-based alignment (52). Normalization incorporated the following four steps: 1) Structural images of each subject were segmented using the 'New Segment' procedure in SPM12.
2) The resulting gray and white matter images were used to derive a study-specific group template. The template was computed from a subgroup of 1.000 subjects, which were part of the subjects included in the present study. 3) An affine transformation was applied to map the group template to MNI space. 4) Subject-to-template and template-to-MNI transformations were combined to map the functional images to MNI space. The functional images were smoothed with an isotropic 8 mm full-width at half-maximum (FWHM) Gaussian filter. Normalized functional images were masked using information from their respective T1 anatomical file as follows. At first, the three-tissue classification probability maps of the "Segment" procedure (grey matter, white matter, and CSF) were summed to define the mask. The mask was binarized, dilated and eroded with a 3 × 3 × 3 voxels kernel using fslmaths (FSL) to fill in potential small holes. The previously computed DARTEL flowfield was used to normalize the brain mask to MNI space, at the spatial resolution of the functional images. The resulting non-binary mask was thresholded at 50% and applied to the normalized functional images. Consequently, the implicit intensity-based masking threshold usually employed to compute a brain mask from the functional data during the first level specification (spm_get_defaults('mask.thresh'), by default fixed at 0.8) was not needed any longer and set to a lower value of 0.05.